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Decoding mammalian gene regulatory p...

Ji, Hongkai.

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Decoding mammalian gene regulatory programs through efficient microarray, ChIP-chip and sequence analysis.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Decoding mammalian gene regulatory programs through efficient microarray, ChIP-chip and sequence analysis./
Author:	Ji, Hongkai.
Description:	172 p.
Notes:	Adviser: Wing Hung Wong.
Contained By:	Dissertation Abstracts International68-05B.
Subject:	Biology, Bioinformatics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3265178
ISBN:	9780549039952

Decoding mammalian gene regulatory programs through efficient microarray, ChIP-chip and sequence analysis.
Ji, Hongkai.

Decoding mammalian gene regulatory programs through efficient microarray, ChIP-chip and sequence analysis. - 172 p.

Adviser: Wing Hung Wong.

Thesis (Ph.D.)--Harvard University, 2007.

Knowing how gene regulatory programs are encoded in the genome and executed in living cells is a key to understand human diseases. The goal of the thesis is to explore efficient statistical strategies to dissect mammalian gene regulation.

ISBN: 9780549039952Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Decoding mammalian gene regulatory programs through efficient microarray, ChIP-chip and sequence analysis.
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020 $a 9780549039952
035 $a (UMI)AAI3265178
035 $a AAI3265178
040 $a UMI $c UMI
100 1 $a Ji, Hongkai. $3 1279924
245 1 0 $a Decoding mammalian gene regulatory programs through efficient microarray, ChIP-chip and sequence analysis.
300 $a 172 p.
500 $a Adviser: Wing Hung Wong.
500 $a Source: Dissertation Abstracts International, Volume: 68-05, Section: B, page: 3133.
502 $a Thesis (Ph.D.)--Harvard University, 2007.
520 $a Knowing how gene regulatory programs are encoded in the genome and executed in living cells is a key to understand human diseases. The goal of the thesis is to explore efficient statistical strategies to dissect mammalian gene regulation.
520 $a An empirical hierarchical Bayes approach was proposed to analyze gene expression data collected from microarray experiments. Through a closed-form variance shrinkage estimator, information from multiple genes is pooled to increase the statistical power of multiple hypothesis testing. The approach allows various types of subject matter knowledge to be incorporated conveniently. Caveats in controlling false discovery rate (FDR) will be discussed. When variance shrinkage estimator is employed, inappropriate use of permutations may result in underestimation of FDR.
520 $a Based on the hierarchical empirical Bayes approach, a TileMap method was developed for the tiling array data analysis. The method combines the hierarchical model with a Moving Average (MA) method or a Hidden Markov Model (HMM). Unbalanced Mixture Subtraction (UMS) was proposed to provide approximate estimates of false discovery rate for MA and model parameters for HMM. Applying TileMap to ChIP-chip allows one to detect transcription factor binding regions at a 500--2000 base pair resolution level. Systematic evaluations showed that the method significantly increased the performance of protein-DNA binding region detection compared to previously existing methods.
520 $a Finally, a comparative analysis involving six human and mouse transcription factors was performed to explore common characteristics of mammalian chromatin immunoprecipitation data and potential issues in their analysis. The cross-study comparisons revealed the importance of matched genomic controls in the de novo identification of 6--30 base pair long transcription factor binding motifs, raised issues about the interpretation of ubiquitously occurring sequence motifs, and demonstrated the clustering tendency of protein binding regions for certain transcription factors.
520 $a The methods developed here were applied to dissect gene regulatory programs in mouse Sonic Hedgehog (SHH) signaling pathway. Through a combined analysis of gene expression, ChIP-chip and sequence motifs, new enhancers that are targeted by transcription factor GLI1 were discovered.
590 $a School code: 0084.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Statistics. $3 517247
690 $a 0463
690 $a 0715
710 2 $a Harvard University. $3 528741
773 0 $t Dissertation Abstracts International $g 68-05B.
790 $a 0084
790 1 0 $a Wong, Wing Hung, $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3265178